Systems and methods for tracking data lineage and record lifecycle using a distributed ledger are disclosed. In one embodiment, a method for tracking record lifecycle events may include: (1) creating, by a record lifecycle tool, a recordable artifact for a record lifecycle event in a record lifecycle, the recordable artifact comprising data for the record lifecycle event; (2) generating, by the record lifecycle tool, a hash of the data; (3) creating, by the record lifecycle tool, record lifecycle event metadata for the recordable artifact; (4) signing, by the record lifecycle tool, the record lifecycle event metadata; (5) writing the record lifecycle event metadata to supply chain metadata storage at a storage location, wherein the supply chain metadata storage may be cryptographically verifiable and immutable; and (6) writing the hash and an identifier for the storage location in the supply chain metadata store to a present state database.
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2. The method of claim 1, wherein the record lifecycle event comprises at least one of creating a record requirement with record definition in a logical data model, committing a physical data store build script in a source code management tool, capturing a record into a system of record application, completing a data store deployment build, completing a data quality test, distributing data to a data store through a batch job, archiving data, and purging a record.
This invention relates to data management systems, specifically tracking and managing the lifecycle of data records across various stages of their existence. The problem addressed is the lack of a systematic approach to monitor and control the progression of data records through different phases, such as creation, storage, processing, testing, distribution, archiving, and deletion. This can lead to inconsistencies, inefficiencies, and compliance risks in data handling. The invention provides a method for tracking and managing record lifecycle events in a data management system. These events include creating a record requirement with its definition in a logical data model, committing a physical data store build script in a source code management tool, capturing a record into a system of record application, completing a data store deployment build, finishing a data quality test, distributing data to a data store through a batch job, archiving data, and purging a record. Each of these events represents a critical stage in the lifecycle of a data record, ensuring that the data is properly managed from inception to retirement. The method enables organizations to maintain data integrity, ensure compliance with regulations, and improve operational efficiency by systematically tracking these lifecycle events. This approach helps prevent data loss, ensures accurate record-keeping, and supports auditing and reporting requirements.
3. The method of claim 1, wherein the recordable artifact comprises at least one of a record instance identifier, a record of a data store deployment, and an identifier for a data quality test exception.
A system and method for managing data quality in a data store deployment involves generating and tracking recordable artifacts to ensure data integrity. The system addresses the challenge of maintaining accurate and reliable data in large-scale data storage environments by creating artifacts that capture critical information about data records, deployments, and exceptions. These artifacts include unique identifiers for individual record instances, detailed records of data store deployments, and identifiers for data quality test exceptions. The artifacts are used to monitor and validate data quality, detect anomalies, and ensure compliance with predefined standards. By associating these artifacts with specific data records, the system enables efficient tracking and resolution of data quality issues, improving overall data reliability and trustworthiness. The method involves generating these artifacts during data processing, storing them in a structured format, and using them to perform automated or manual quality checks. This approach helps organizations maintain high data integrity, reduce errors, and streamline data management processes.
4. The method of claim 1, wherein the hash of the data comprises a hash of a reference to the data.
A system and method for data integrity verification involves generating a hash of data to ensure its authenticity and integrity. The data may be stored in a distributed or decentralized system, such as a blockchain or a distributed ledger, where verifying data integrity is critical. The method includes generating a hash value for the data, where the hash may be derived from a reference to the data rather than the data itself. This reference could be a pointer, a unique identifier, or a metadata entry that indirectly represents the data. The hash of the reference ensures that any changes to the reference, such as modifications to the data's location or metadata, are detectable. The system may also include a verification mechanism that compares the generated hash with a stored or expected hash value to confirm data integrity. This approach is particularly useful in environments where direct access to the original data is restricted or where data is frequently updated, as it allows for efficient and scalable integrity checks without requiring full data retrieval. The method may be applied in blockchain transactions, secure data storage systems, or any application where tamper-proof data verification is necessary.
5. The method of claim 1, wherein the record lifecycle event metadata comprises at least one of a name for the recordable artifact, an organization identifier, a creation time, recordable artifact data, the hash, a reference to the hash, and an identifier for the record lifecycle tool.
This invention relates to a system for managing and verifying the lifecycle of digital artifacts, particularly in environments requiring high integrity and traceability. The problem addressed is the need to securely record and track the creation, modification, and other lifecycle events of digital artifacts while ensuring their authenticity and immutability. The method involves generating a cryptographic hash of a recordable artifact, such as a document or data file, to create a unique digital fingerprint. This hash is then stored as part of record lifecycle event metadata, which includes additional contextual information. The metadata may contain a name for the artifact, an organization identifier, a creation timestamp, the artifact data itself, the hash, a reference to the hash, or an identifier for the tool used to record the event. This metadata is then securely stored in a tamper-evident manner, ensuring that any subsequent changes to the artifact or its metadata can be detected. The system ensures that the lifecycle of digital artifacts is fully documented and verifiable, preventing unauthorized modifications and providing a reliable audit trail. This is particularly useful in regulated industries where compliance and data integrity are critical. The use of cryptographic hashing and structured metadata allows for efficient verification of artifact authenticity and traceability throughout its lifecycle.
6. The method of claim 1, wherein the record lifecycle event metadata comprises a reference to a prior record lifecycle event.
A system and method for managing record lifecycle events in a data processing environment. The invention addresses the challenge of tracking and organizing the history of changes to digital records, ensuring data integrity, compliance, and auditability. The method involves capturing metadata associated with lifecycle events, such as creation, modification, deletion, or access of a record. This metadata includes timestamps, user identifiers, and event types, enabling a comprehensive audit trail. The invention enhances this by incorporating a reference to a prior record lifecycle event within the metadata of a current event. This linkage allows for sequential tracking of related events, improving traceability and contextual understanding of record changes. The system may also include mechanisms for storing, retrieving, and analyzing this metadata to support compliance reporting, forensic investigations, and operational workflows. The method ensures that each event is not only recorded but also connected to its predecessor, providing a clear chain of custody for records. This approach is particularly useful in regulated industries where maintaining an accurate and verifiable history of record changes is critical. The invention may be implemented in software, hardware, or a combination thereof, and can be integrated into existing data management systems.
7. The method of claim 1, wherein the supply chain metadata store comprises at least one of immutable queues, hashgraphs, blockchain-based systems, stream processing services, NoSQL databases, and relational databases.
This invention relates to supply chain management systems that track and process metadata to improve transparency, security, and efficiency in supply chain operations. The system addresses challenges in traditional supply chain tracking, such as data tampering, lack of real-time visibility, and inefficiencies in data processing. The core method involves storing and managing supply chain metadata in a specialized metadata store designed to ensure data integrity, scalability, and accessibility. The metadata store can be implemented using various technologies, including immutable queues, hashgraphs, blockchain-based systems, stream processing services, NoSQL databases, and relational databases. Immutable queues ensure that once data is recorded, it cannot be altered, providing a tamper-proof record of transactions. Hashgraphs and blockchain-based systems offer decentralized, cryptographically secure ledgers that enhance trust and transparency. Stream processing services enable real-time analysis of supply chain data, allowing for immediate insights and decision-making. NoSQL databases provide flexible, scalable storage for unstructured or semi-structured metadata, while relational databases offer structured querying capabilities for complex supply chain analytics. By leveraging these diverse storage technologies, the system ensures that supply chain metadata is stored securely, processed efficiently, and remains accessible for auditing and analysis. This approach enhances supply chain resilience, reduces fraud, and improves operational efficiency by providing a reliable and verifiable record of all transactions and events.
8. The method of claim 1, wherein the present state database comprises a key/value pair database.
A system and method for managing state data in a distributed computing environment addresses the challenge of efficiently tracking and retrieving state information across multiple nodes. The system includes a state management module that monitors and updates a present state database, which stores current state information for various components or processes. The present state database is implemented as a key/value pair database, allowing for fast lookups and updates using unique keys associated with specific state data. The state management module periodically checks the state of components, compares them to stored values, and updates the database as needed. This ensures that the system maintains an accurate and up-to-date representation of the current state across all nodes. The key/value structure enables efficient querying and retrieval of state information, improving performance and scalability in distributed systems. The system may also include a historical state database to track changes over time, allowing for analysis and debugging. The method ensures consistency and reliability in state management, reducing errors and improving system performance.
10. The method of claim 9, wherein the present state database comprises a key/value pair database.
A system and method for managing state data in a distributed computing environment addresses the challenge of efficiently tracking and retrieving state information across multiple nodes. The system includes a state management module that monitors and updates the state of various components or processes within the system. The state data is stored in a present state database, which is structured as a key/value pair database. This database allows for fast lookups and updates by associating unique keys with corresponding values representing the current state of a component or process. The key/value structure simplifies data retrieval and ensures that state information can be quickly accessed and modified as needed. The system also includes a state transition module that processes state changes, ensuring that the present state database is updated accurately and consistently. This approach improves system reliability and performance by providing a centralized and efficient way to manage state data in distributed environments. The use of a key/value pair database further enhances scalability and flexibility, allowing the system to handle large volumes of state data efficiently.
11. The method of claim 9, wherein the at least one storage location comprises a block in a blockchain-based system.
Digital data management. The invention addresses secure and immutable storage of digital information. Specifically, it relates to a system and method for storing data, where at least one designated storage location is implemented as a block within a blockchain-based system. This approach leverages the inherent characteristics of blockchain technology, such as decentralization, immutability, and cryptographic security, to ensure the integrity and trustworthiness of the stored data. The use of a blockchain block as a storage location implies that the data, or a reference to it, is recorded in a cryptographically secured and distributed ledger, making it resistant to tampering and unauthorized modification. This provides a robust mechanism for maintaining a verifiable and permanent record of digital assets or information.
12. The method of claim 9, wherein the lineage comprises downstream lineage for the record instance.
A system and method for tracking data lineage in a computing environment addresses the challenge of maintaining visibility into the origin, transformations, and dependencies of data records as they move through complex data processing pipelines. The invention provides a mechanism to capture and display the downstream lineage of a record instance, showing how the record is used or transformed in subsequent processes. This helps organizations ensure data integrity, comply with regulatory requirements, and troubleshoot data issues by tracing the flow of data from its source to its final destination. The method involves identifying a record instance in a data processing system, determining its downstream lineage by analyzing how the record is referenced or modified in subsequent operations, and generating a visual or structured representation of this lineage. The downstream lineage may include intermediate transformations, dependencies on other data records, and final outputs where the record is utilized. This approach enables users to understand the impact of changes to a record and verify the accuracy of data throughout its lifecycle. The invention is particularly useful in environments where data undergoes multiple stages of processing, such as in data lakes, data warehouses, or machine learning pipelines.
15. The system of claim 14, wherein the record lifecycle event comprises at least one of creating a record requirement with record definition in a logical data model, committing a physical data store build script in a source code management tool, capturing a record into a system of record application, completing a data store deployment build, completing a data quality test, distributing data to a data store through a batch job, archiving data, and purging a record.
This invention relates to a system for managing record lifecycle events in data management environments. The system addresses the challenge of tracking and automating various stages in the lifecycle of data records, ensuring consistency, compliance, and efficiency in data handling processes. The system monitors and processes multiple record lifecycle events, including creating record requirements with definitions in a logical data model, committing physical data store build scripts in a source code management tool, capturing records into a system of record application, completing data store deployment builds, finishing data quality tests, distributing data to a data store through batch jobs, archiving data, and purging records. These events are tracked to maintain data integrity, enforce governance policies, and streamline data operations. The system integrates with various tools and applications involved in data management, such as source code management systems, data quality testing frameworks, and batch processing systems. By automating the detection and handling of these lifecycle events, the system reduces manual intervention, minimizes errors, and ensures that data records are properly managed from creation to retirement. This approach enhances data governance, improves operational efficiency, and supports compliance with regulatory requirements.
16. The system of claim 14, wherein the hash of the data comprises a hash of a reference to the data.
A system for data integrity verification and management involves generating and storing cryptographic hashes of data to ensure its authenticity and integrity. The system includes a data storage component that stores data and a hash generation module that computes a cryptographic hash of the data. The system also includes a verification module that compares the computed hash with a stored hash to detect any modifications or tampering. In some implementations, the system further includes a reference tracking module that generates a hash of a reference to the data rather than the data itself. This reference may include metadata, a pointer, or an identifier associated with the data, allowing for efficient verification without processing the entire data set. The system may also include a distributed ledger or blockchain component to record hash values and references in an immutable manner, ensuring tamper-proof verification. The system is particularly useful in environments where data integrity is critical, such as financial transactions, supply chain tracking, or digital forensics. By using cryptographic hashes, the system provides a secure and reliable way to verify the authenticity of data and detect unauthorized changes.
17. The system of claim 14, wherein the record lifecycle event metadata comprises a reference to a prior record lifecycle event.
A system for managing record lifecycle events in a data processing environment addresses the challenge of tracking and correlating changes to records over time. The system captures metadata associated with lifecycle events, such as creation, modification, or deletion of records, to ensure data integrity and auditability. The metadata includes timestamps, event types, and user identifiers, enabling reconstruction of record histories and compliance with regulatory requirements. A key feature of the system is the inclusion of a reference to a prior record lifecycle event within the metadata of a subsequent event. This linkage allows the system to establish a chronological sequence of events, facilitating traceability and impact analysis. For example, if a record is modified, the metadata for that event can reference the previous modification or creation event, creating a chain of related actions. This ensures that users can track how a record evolved over time, identify dependencies, and verify the integrity of the data. The system may also include a storage component for retaining the metadata and a processing module to generate and associate the metadata with the corresponding records. The storage component ensures that the metadata is persistently stored and retrievable for auditing or reporting purposes. The processing module automates the capture and linking of event metadata, reducing manual effort and minimizing errors. By maintaining a linked history of record lifecycle events, the system enhances transparency, supports forensic investigations, and simplifies compliance with data governance policies. This approach is particularly valuable in industries where audit trails and data provenance are critical, such as finance, healthcare, and regulatory compliance.
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January 7, 2021
April 2, 2024
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